ITU-T have defined in their Recommendation T.44 the Mixed Raster Content (MRC) model. By using this model, it would be possible to compress color and grayscale document images with a high compression rate, a good legibility of the text and a good rendering of the pictures. The MRC Model divides the document image into 3 layers: the binary mask layer, the foreground layer and the background layer. The mask layer is a binary image, the background and foreground layers are color (or grayscale) images. An ON pixel in the binary mask layer indicates that, when decompressing, the color (or grayscale) has to be taken from the foreground layer. An OFF pixel in the binary mask layer indicates that, when decompressing, the color (or grayscale) has to be taken from the background layer. However, ITU-T T.44 does not specify the method of the division into layers.
From U.S. Pat. No. 5,778,092 a first technique for compressing a color or gray scale pixel map representing a document is known, corresponding to the MRC model. The pixel map is decomposed into a three-plane representation comprising a reduced-resolution foreground plane, a reduced-resolution background plane, and a high-resolution binary selector plane. The foreground plane contains the color or gray scale information of foreground items such as text and graphic elements. The background plane contains the color or gray scale information for the “background” of the page and the continuous tone pictures that are contained on the page. The selector plane stores information for selecting from either the foreground plane or background plane during decompression. Each of the respective planes is compressed using a compression technique suitable for the corresponding data type.
From U.S. Pat. No. 6,731,800 another technique is known for compressing scanned, colored and gray-scale documents, in which the digital image of the scanned document is divided into three image planes, namely a foreground image, a background image and a binary mask image. The mask image describes which areas of the document belong to the foreground and which to the background. In order to generate the mask image, a locally variable threshold value image is generated from the defined reduced original document with an adaptive threshold method, and brought back once again to the size of the original document. With this technique, also inverse text (light text on a dark background) can be detected. The inverse text is detected by the concept of “holes”. A “hole” is a foreground region or blob which touches a different foreground region which has already been entered. This method requires a lot of memory since all blobs have to be tracked and is time consuming since it has to be checked if the blobs are touching each other. In addition both the “black” blobs and the “white” blobs have to be recorded.
From U.S. Pat. No. 6,748,115 an image compression technique is known, which employs selecting a gray level threshold value for converting a gray level digital image input into a bi-level input which minimizes weak connectivity, wherein weak connectivity comprises a checkerboard pattern found in a 2×2 array or neighborhood of pixels. The threshold value for the conversion is determined by traversing the array of pixels comprising the document in a single path, examining successive 2×2 neighborhoods and incrementing a plus register for the gray level value which a checkerboard pattern first appears and incrementing a minus register for the gray level value at which the checkerboard pattern no longer exists.
These image compression techniques however have the disadvantage that the achieved compression rates are insufficient. Often also the quality of the reconstructed image, e.g. the legibility of the text or the rendering of the pictures is affected by the compression technique.
From U.S. Pat. No. 5,835,638 a method and apparatus are known for comparing symbols extracted from binary images of text for classifying into equivalence classes. A Hausdorff-like method is used for comparing symbols for similarity. When a symbol contained in a bitmap A is compared to a symbol contained in a bitmap B, it is determined whether or not the symbol in bitmap B fits within a tolerance into a dilated representation of the symbol in bitmap A with no excessive density of errors and whether the symbol in bitmap A fits within a tolerance into a dilated representation of the symbol in bitmap B with no excessive density of errors. If both tests are passed, an error density check is performed to determine a match.
This known symbol comparison method has the disadvantage that in many cases a match may be returned where in fact a mismatch occurs.